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Analysis of Stepped-Wedge Cluster Randomized Trials: A Tutorial Using Marginal Models.

Elizabeth L Turner1,2, John S Preisser3,4, Ying Zhang3

  • 1Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.

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|March 20, 2026
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Summary
This summary is machine-generated.

Stepped-wedge cluster randomized trials (SW-CRTs) benefit from advanced marginal modeling techniques. This tutorial details paired generalized estimating equations (GEE) and matrix-adjusted estimating equations (MAEE) for improved analysis of SW-CRTs.

Keywords:
crossover CRTexponential decay correlation structuregeneralized estimating equations (GEE)intracluster correlation coefficientmatrix‐adjusted estimating equations (MAEE)multi‐period CRTnested exchangeable correlation structuresmall‐sample corrections

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Epidemiology

Background:

  • Stepped-wedge cluster randomized trials (SW-CRTs) are increasingly used for intervention evaluation.
  • Traditional marginal modeling of SW-CRTs often uses suboptimal correlation structures, especially in multi-period designs.
  • Recent methodological advancements address these limitations, offering more robust analysis.

Purpose of the Study:

  • To survey recent developments in marginal modeling for SW-CRTs.
  • To provide practical guidance and case studies for applying these advanced methods.
  • To enable researchers to implement paired generalized estimating equations (GEE) and matrix-adjusted estimating equations (MAEE).

Main Methods:

  • Focus on multi-parameter within-cluster correlation structures.
  • Detailed explanation of paired GEE for simultaneous estimation of mean and correlation parameters.
  • Application of matrix-adjusted estimating equations (MAEE) for bias correction in small cluster settings.

Main Results:

  • The tutorial surveys methodological developments over the past fifteen years.
  • Case studies demonstrate the implementation of GEE/MAEE for SW-CRT analysis.
  • The methods are applicable to various SW-CRT designs, including cohorts and repeated cross-sectional samples.

Conclusions:

  • Advanced marginal modeling techniques like GEE and MAEE enhance the analysis of SW-CRTs.
  • These methods provide more accurate estimation of intervention effects and correlation structures.
  • The tutorial empowers applied researchers to utilize these sophisticated statistical tools effectively.